Machine Learning Systems Engineer

This is a fully remote position, open to applicants in Pakistan.

📋 Description

• Design, develop, and manage scalable backend services for a media intelligence platform, emphasizing clean, maintainable, and production-ready systems.

• Take ownership of essential backend components from system design and API agreements to implementation, deployment, monitoring, and continuous improvement.

• Influence architectural choices related to APIs, processing pipelines, distributed computing, storage, search, observability, cloud infrastructure, and model-serving workflows.

• Create data models and storage strategies for media assets, generated metadata, embeddings, processing jobs, model outputs, search indexes, and audit logs.

• Develop distributed, event-driven processes for media handling using queues and pub/sub technologies such as SQS, Kafka, Pub/Sub, or similar systems.

• Apply dependable asynchronous processing techniques, including retries, idempotency, dead-letter queues, backpressure management, and fault-tolerant job execution.

• Spearhead the development and enhancement of metadata extraction, content analysis, scene detection, transcription, embedding generation, and multimodal AI inference workflows.

• Collaborate with machine learning engineers, data scientists, or external model providers to evaluate models, analyze quality/latency trade-offs, and safely implement model upgrades.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field with equivalent hands-on experience.

• 5-7+ years of backend engineering experience, preferably in developing scalable distributed systems, media platforms, data pipelines, or high-throughput backend services.

• Previous experience managing significant backend modules from start to finish, encompassing architecture, implementation, deployment, monitoring, and operational management.

• 3+ years in integrating AI/ML inference systems into backend workflows, including model APIs, embedding pipelines, OCR, speech-to-text, scene detection, or multimodal model outputs.

• Practical experience in creating AI-driven processing pipelines for analyzing images, videos, audio, or text.

• Hands-on experience with production model optimization, particularly for image, video, embedding, or multimodal models, involving batching, caching, quantization, prompt optimization, routing strategies, latency reduction, and cost management.

• Strong preference for candidates with prior experience in vector search, semantic search, media retrieval, or similarity-matching systems.

• Proven experience in mentoring engineers, leading technical discussions, and shaping architectural decisions across backend, infrastructure, and AI/ML workflows.

🏝️ Benefits

• Flexible work arrangements

• Professional development opportunities